Today, digital systems are used to represent and process virtually all types of media in commercial, entertainment, educational and other fields. For example, visual media such as photographs, movies, video and animations are largely handled in the digital domain. Similarly, audio media such as speech, music, sound effects, etc., are also recorded, edited, produced, copied and transferred in the digital domain. Other applications for digital systems include the growing e-commerce marketplace where buyers can select from millions of different products and services online, such as via the Internet.
Although digital systems provide many benefits, the incredible speed and versatility of such systems to access and manipulate media and other information is often overwhelming to a human user. For example, in the visual media areas many types of non-linear editing operations can be performed to create a movie or video, or to modify, correct or otherwise change image areas or sequences of images. Art directors, graphics designers, engineers, producers or other users of computer workstations can perform dozens of different operations on any of millions of pixel elements in a typical image or series of images. Such operations include color changes, sharpness corrections; visual effects, combinations or composites of different images, scene transitions, etc.
Many of the objects in today's visual media are created digitally in the first place. For example, a computer-generated model may be used to represent an object or character in a movie scene or still picture. Not only do human user's have complete control of the model's shape, color scheme, texture, lighting and movement; but once a scene is rendered, the rendered sequence of images can be further modified as described above.
The process of creating visual media is multiplied in complexity since many types of manipulation can affect the outcome of subsequent manipulations. For example, a color correction may affect a later chromakey operation, a contrast adjustment can change an anti-aliasing filterj's effect, and so on. Similar layers of complexity exist in creating and processing other digital media such as audio.
Amidst all of the complexity, a human user must decide which operations to perform to achieve a desired result. The process of applying operations to media is often achieved as a change in attributes, or parameters, of the media. For example, an image can have parameters of color components, filter effects, transitions, etc. Each parameter typically has a single numeric value. Changing the parameter's value creates a change in the quality of the image associated with the parameter. Thus, increasing the value of a parameter that expresses the amount of red in an image will cause the image to appear more red.
Parameters can be used to describe many aspects of digital media and information. Because parameters are a useful way to describe and modify digital media, several prior art approaches exist to allow a human user to modify media parameters and to present media to a user based on original and modified parameters.
One approach is to use alphanumeric text to name a parameter and the parameter's value. This is useful where a parameter can easily be described with a text label and where the effect of modifying a parameter value is intuitive. For example, when intensity of color components of an image is specified as a function of red, green and blue (RGB) additive colors, the intensity can range from 0 to 100 percent. Thus, a user can type in a value for each of “green,” “red” and “blue” fields on a display and have some notion of the outcome of modifying the parameter values. Similarly, with audio volume, a “volume” parameter can receive a value in percent, decibels, etc.
Once a parameter or parameters has been modified, the user can direct the media to be presented using the new parameters. This can mean an image is redisplayed using the new color components intensities or that an audio segment is played back at a new volume level.
Although alphanumeric parameter presentation and modification is easily implemented, it is often difficult and cumbersome for a user to work with such an interface. This is partly due to the sheer number of different parameters that may be present in a media presentation. Also, some parameter labels do not effectively convey an intuitive purpose. For example, RGB, luminance and chrominance are parameters that are used to describe images. Most users who have not spent a lot of time working with such parameters will not be able to accurately predict what effect modifying a parameter will have on the presentation. This leads to a trial-and-error approach.
One improvement over simple alphanumeric presentation and modification of parameters is to provide the user with more versatile, graphical, input controls such as slider bars, radio buttons, knobs, etc. Another approach is to provide the user with a curve or curves on a chart or grid. The curves correspond to values of the parameter with respect to some other variable, such as time, location, etc. The user can drag points on the curve to modify the curve. Thus, the user is able to adjust, for example, RGB components over time or over image areas without having to specify many values for many different points in an image. Other methods of modifying the curves include using math functions to modify the curve's behavior, or to use parameterized math functions that define the curve and change the curve's parameters (as opposed to the parameters that the curve is describing).
This graphical approach to change the values, or functions, on a curve that represents one or more parameters is not without drawbacks. In the first place, a user often needs to know which parameter to change just to call up the necessary display and tools to change the parameter. For example, a user may decide that an area of an image is “blurry” but will not know the parameter to modify in order to make the image less blurry. In some cases, a user may not like an image but will not even be able to describe in words exactly what is wrong with the image. This is especially true when a team of users is attempting to create a media presentation. For example, an art director and digital artist may be collaborating in making a change to an image. The art director may need to express a change to the digital artist but will be unable to do so effectively since the art director typically has less knowledge of image parameters and their effects upon the image.
Thus, it is desirable to provide an invention that improves upon the prior art.